کنفرانس بین المللی فناوری و مدیریت انرژی

صفحه اصلی / نهمین کنفرانس بین المللی فناوری و مدیریت انرژی

StateEVMan: Advanced Predictive Ensemble Optimization of Electric Vehicle Charging Stations

نویسندگان :

Ashkan Safari¹ Hamed Kheirandish Gharehbagh² Morteza Nazari-Heris³ Hamed Kharrati⁴ Afshin Rahimi⁵

1- University of Tabriz 2- University of Tabriz 3- Lawrence Technological University 4- University of Windsor 5- University of Windsor

کلمات کلیدی :

Electric Vehicle Charging Stations،Forecasting،Optimization،Doubly-Fed LSTM،Precision Forecasting

چکیده :

Optimizing electric vehicle charging stations through advanced predictive ensemble techniques is essential for enhancing efficien-cy, reducing operational costs, and promoting the widespread adoption of electric vehicles. This approach plays a pivotal role in ensuring seamless charging experiences, thereby advancing the transition to a sustainable and eco-friendly transportation sys-tem. By this regard, the proposed paper presents StateEVMan, a novel approach employing doubly-fed Long Short-Term Memory (LSTM) techniques in conjunction with a comprehensive Electric Vehicle (EV) station dataset. Utilizing stacked ensemble learning, the model predicts three key performance indicators (KPIs): Charging Time [Hour], Total Power Output [kWh], and Total Cost [$]. The study assesses the model's performance using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) metrics across a dataset comprising 10,185 data points. Notably, the model achieves accurate predictions for these KPIs, demonstrating its robust forecasting capabilities. StateEVMan emerges as a consid-erable tool for optimizing EV charging station operations and enhancing efficiency.